A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials

Jayne F Tierney, David J Fisher, Claire L Vale, Sarah Burdett, Larysa H Rydzewska, Ewelina Rogozińska, Peter J Godolphin, Ian White and Mahesh K B Parmar

PLOS Medicine, 2021, vol. 18, issue 5, 1-14

Abstract: Background: The vast majority of systematic reviews are planned retrospectively, once most eligible trials have completed and reported, and are based on aggregate data that can be extracted from publications. Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases. We present a collaborative framework for prospective, adaptive meta-analysis (FAME) of aggregate data to provide results that are less prone to bias. Also, with FAME, we monitor how evidence from trials is accumulating, to anticipate the earliest opportunity for a potentially definitive meta-analysis. Methodology: We developed and piloted FAME alongside 4 systematic reviews in prostate cancer, which allowed us to refine the key principles. These are to: (1) start the systematic review process early, while trials are ongoing or yet to report; (2) liaise with trial investigators to develop a detailed picture of all eligible trials; (3) prospectively assess the earliest possible timing for reliable meta-analysis based on the accumulating aggregate data; (4) develop and register (or publish) the systematic review protocol before trials produce results and seek appropriate aggregate data; (5) interpret meta-analysis results taking account of both available and unavailable data; and (6) assess the value of updating the systematic review and meta-analysis. These principles are illustrated via a hypothetical review and their application to 3 published systematic reviews. Conclusions: FAME can reduce the potential for bias and produce more timely, thorough, and reliable systematic reviews of aggregate data. Jayne Tierney and coauthors discuss FAME, an approach for adaptive meta-analysis of data from randomised trials.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmed00:1003629

DOI: 10.1371/journal.pmed.1003629

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